Enhanced Knowledge Encoder v2.0.0: Complete replacement with self-learning and continual learning capabilities

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	# Enhanced Knowledge Encoder v2.0.0

	A revolutionary self-learning and continual learning model that completely replaces previous features with advanced capabilities for document understanding and knowledge extraction.

	## 🚀 Enhanced Features (v2.0.0)

	### 🧠 Neural Memory System
	- Persistent Knowledge Storage: No external databases required
	- Intelligent Memory Management: Automatic memory slot allocation and optimization
	- Memory Utilization Tracking: Real-time monitoring of knowledge storage efficiency

	### 📚 Continual Learning
	- Document-Based Learning: Model improves with each new document
	- Adaptive Learning Rate: Dynamic adjustment based on document quality
	- Learning Statistics: Comprehensive tracking of learning progress and metrics

	### 🚀 Self-Improving Inference
	- Knowledge Fusion: Intelligent combination of memory and current input
	- Advanced Attention Mechanisms: Multi-head attention with memory integration
	- Quality-Aware Processing: Document quality assessment and learning

	### 🔍 Advanced Attention Mechanisms
	- Memory-Aware Attention: Attention that considers stored knowledge
	- Multi-Head Memory Attention: Parallel attention across knowledge dimensions
	- Dynamic Attention Weights: Adaptive attention based on input relevance

	### 💡 Intelligent Tokenization
	- Subword Tokenization: BPE-like tokenization for better word handling
	- Learning Tokenizer: Vocabulary expansion based on document learning
	- Quality-Weighted Learning: Token importance based on document quality

	## 📚 Use Cases

	- Document Understanding: Comprehensive analysis of complex documents
	- Knowledge Extraction: Intelligent extraction of key information
	- Continual Learning: Models that improve over time with new data
	- Intelligent Q&A Systems: Context-aware document question answering
	- Research Automation: Automated research and analysis workflows
	- Content Analysis: Deep understanding of text content and structure

	## 🔧 Quick Start

	### Installation

	```bash
	# Install from Hugging Face
	pip install git+https://huggingface.co/PoornaChandra797/knowledge-encoder

	# Or install locally
	git clone https://huggingface.co/PoornaChandra797/knowledge-encoder
	cd knowledge-encoder
	pip install -e .
	```

	### Basic Usage

	```python
	from knowledge_encoder import EnhancedKnowledgeEncoder, EnhancedTokenizer

	# Initialize enhanced model and tokenizer
	model = EnhancedKnowledgeEncoder(
	vocab_size=1000,
	hidden_size=256,
	num_attention_heads=8,
	num_hidden_layers=4,
	memory_size=1000,
	learning_rate=1e-4
	)

	tokenizer = EnhancedTokenizer(
	vocab_size=1000,
	min_frequency=1,
	max_word_length=50
	)

	# Learn from documents
	document_text = "Your document content here..."
	document_embeddings = model.encode_text(document_text)

	# Continual learning
	learning_result = model.learn_from_document(document_embeddings, document_quality=0.9)
	tokenizer.learn_from_document(document_text, document_quality=0.9)

	# Get intelligent responses
	query_text = "What is the main topic?"
	query_embeddings = model.encode_text(query_text)
	response = model.forward(query_embeddings)

	# Retrieve knowledge
	retrieved_knowledge, similarities = model.retrieve_knowledge(query_embeddings, top_k=5)

	# Get learning statistics
	stats = model.get_learning_statistics()
	print(f"Learning sessions: {stats['learning_metrics']['learning_sessions']}")
	print(f"Memory utilization: {stats['learning_metrics']['memory_utilization']:.2f}")
	```

	## 📊 Advanced Features

	### Learning from Documents

	```python
	# Batch learning from multiple documents
	documents = [
	("Document 1 content...", 0.9),
	("Document 2 content...", 0.8),
	("Document 3 content...", 0.95)
	]

	for doc_text, quality in documents:
	# Learn from document
	doc_embeddings = model.encode_text(doc_text)
	learning_result = model.learn_from_document(doc_embeddings, quality)

	# Learn tokenization patterns
	tokenizer.learn_from_document(doc_text, quality)

	print(f"Learned from document with quality {quality}: {learning_result}")
	```

	### Knowledge Retrieval

	```python
	# Retrieve relevant knowledge
	query = "What are the key concepts?"
	query_embeddings = model.encode_text(query)

	# Get top-k most relevant knowledge
	knowledge, similarities = model.retrieve_knowledge(query_embeddings, top_k=10)

	print(f"Retrieved {len(knowledge)} knowledge items")
	for i, (k, s) in enumerate(zip(knowledge, similarities)):
	print(f"Knowledge {i+1}: Similarity {s:.3f}")
	```

	### Learning Statistics

	```python
	# Comprehensive learning statistics
	stats = model.get_learning_statistics()

	print("=== Model Information ===")
	print(f"Total parameters: {stats['model_info']['total_parameters']:,}")
	print(f"Memory size: {stats['model_info']['memory_size']}")
	print(f"Learning rate: {stats['model_info']['learning_rate']}")

	print("\n=== Learning Metrics ===")
	print(f"Total documents: {stats['learning_metrics']['total_documents']}")
	print(f"Learning sessions: {stats['learning_metrics']['learning_sessions']}")
	print(f"Memory utilization: {stats['learning_metrics']['memory_utilization']:.2f}")
	print(f"Knowledge diversity: {stats['learning_metrics']['knowledge_diversity']:.2f}")

	print("\n=== Recent Learning History ===")
	for session in stats['learning_history'][-5:]:
	print(f"Session: Loss {session['loss']:.4f}, Quality {session['document_quality']:.2f}")
	```

	## 🏗️ Architecture

	### Enhanced Model Structure

	```
	EnhancedKnowledgeEncoder
	├── Token Embeddings
	├── Positional Encoding
	├── Transformer Encoder Layers
	├── Neural Memory System
	│ ├── Knowledge Memory
	│ ├── Memory Attention
	│ └── Memory Gate
	├── Knowledge Fusion
	├── Learning Mechanisms
	│ ├── Optimizer (AdamW)
	│ ├── Scheduler (CosineAnnealing)
	│ └── Learning Metrics
	└── Output Projections
	```

	### Key Components

	- Neural Memory: Persistent storage of learned knowledge
	- Memory Attention: Intelligent retrieval of relevant knowledge
	- Knowledge Fusion: Combination of memory and current input
	- Continual Learning: Ongoing model improvement
	- Quality Assessment: Document quality-based learning

	## 📈 Performance

	### Memory Efficiency
	- Dynamic Memory Allocation: Automatic optimization of memory usage
	- Memory Utilization Tracking: Real-time monitoring of efficiency
	- Adaptive Memory Management: Intelligent memory slot allocation

	### Learning Efficiency
	- Quality-Weighted Learning: Better learning from high-quality documents
	- Adaptive Learning Rate: Dynamic adjustment for optimal learning
	- Learning Statistics: Comprehensive tracking of learning progress

	### Inference Performance
	- Enhanced Attention: Faster and more accurate attention mechanisms
	- Memory Integration: Efficient knowledge retrieval and integration
	- Optimized Forward Pass: Streamlined inference pipeline

	## 🔄 Backward Compatibility

	All previous imports continue to work seamlessly:

	```python
	# Old imports still work
	from knowledge_encoder import KnowledgeEncoder, SimpleTokenizer
	from knowledge_encoder import load_model, save_model, validate_model

	# New enhanced imports
	from knowledge_encoder import EnhancedKnowledgeEncoder, EnhancedTokenizer
	from knowledge_encoder import load_enhanced_model, save_enhanced_model, validate_enhanced_model
	```

	## 🧪 Testing

	### Model Validation

	```python
	from knowledge_encoder import validate_enhanced_model

	# Validate enhanced model
	is_valid = validate_enhanced_model("path/to/model.pth")
	print(f"Model validation: {'✅ PASSED' if is_valid else '❌ FAILED'}")
	```

	### Inference Testing

	```python
	from knowledge_encoder import test_enhanced_model_inference

	# Test model inference
	results = test_enhanced_model_inference("path/to/model.pth", "Test document content")
	print(f"Test results: {results}")
	```

	### Performance Benchmarking

	```python
	from knowledge_encoder import benchmark_enhanced_model

	# Benchmark model performance
	benchmark_results = benchmark_enhanced_model("path/to/model.pth")
	print(f"Benchmark results: {benchmark_results}")
	```

	## 📦 Package Management

	### Creating Model Packages

	```python
	from knowledge_encoder import create_enhanced_model_package

	# Create distribution package
	package_path = create_enhanced_model_package(
	"path/to/model.pth",
	"output/package",
	include_tokenizer=True
	)
	print(f"Package created at: {package_path}")
	```

	### Saving Enhanced Models

	```python
	# Save with all learning state
	model.save_pretrained("enhanced_model_v2.pth")

	# Save tokenizer with learning state
	tokenizer.save_pretrained("enhanced_tokenizer_v2/")
	```

	## 🌟 Key Advantages

	1. No External Dependencies: Self-contained neural memory system
	2. Continual Improvement: Model gets better with each document
	3. Intelligent Learning: Quality-aware document processing
	4. Advanced Architecture: State-of-the-art transformer design
	5. Easy Integration: Simple API for any application
	6. Production Ready: Stable, tested, and optimized
	7. Open Source: Free to use and modify
	8. Active Development: Ongoing improvements and updates

	## 🤝 Contributing

	We welcome contributions! Please see our contributing guidelines for more information.

	## 📄 License

	This project is licensed under the MIT License - see the LICENSE file for details.

	## 🙏 Acknowledgments

	- Built with PyTorch and Transformers
	- Inspired by modern neural network architectures
	- Designed for real-world document understanding applications

	## 📞 Support

	- Documentation: [Hugging Face Hub](https://huggingface.co/PoornaChandra797/knowledge-encoder)
	- Issues: [GitHub Issues](https://github.com/Poornachandra-k/knowledge-encoder/issues)
	- Email: poornachandrak@ideyalabs.com

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	Enhanced Knowledge Encoder v2.0.0 - Revolutionizing document understanding with self-learning and continual learning capabilities.